In this project, I use machine learning to optimize the transport integral of tetracene. Organic semiconductors are responsible for the OLED panels found on high end TV's and cell phone screens. Naturally, a significant amount of research in this field revolves around finding new state of the art materials that are more efficient and performant. Computing electronic properties of theoretical materials, such as the molecular transport integral, is computationally expensive. Using machine learning, we can predict (and optimize) the transport integral of a material, which can then be used to calculate the charge carrier mobility using Monte Carlo simulations.
Similar work was first published by Lederer et al. in 2019 using pentacene. In this project, I replicate his work (starting from scratch but following his methodology) and continue to improve upon it using feature engineering to explore the relationship between the material topology and its conducting properties. The ML_coulomb file takes the coulomb interactions as a pickle file (generated by the other .py file) and predicts the transport integrals. My predictions resulted in a RSS < 1 when tested against the unseen professor's test data for final grading. The best method for the predictions was a neural net made using Keras.
This work was completed as a project for the course "Computational Materials Design" at the Technical University of Munich.